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Using Conditional Kernel Density Estimation for Wind Power Density Forecasting

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Cited by:

  1. Zhang, Yao & Wang, Jianxue & Wang, Xifan, 2014. "Review on probabilistic forecasting of wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 255-270.
  2. Dahl, Christian M. & Effraimidis, Georgios & Pedersen, Mikkel H., 2019. "Nonparametric wind power forecasting under fixed and random censoring," Energy Economics, Elsevier, vol. 84(C).
  3. Song, Zhe & Jiang, Yu & Zhang, Zijun, 2014. "Short-term wind speed forecasting with Markov-switching model," Applied Energy, Elsevier, vol. 130(C), pages 103-112.
  4. Liu, Yanli & Wang, Junyi, 2022. "Transfer learning based multi-layer extreme learning machine for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 312(C).
  5. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
  6. Kusiak, Andrew & Zhang, Zijun & Verma, Anoop, 2013. "Prediction, operations, and condition monitoring in wind energy," Energy, Elsevier, vol. 60(C), pages 1-12.
  7. Fresoli, Diego E. & Ruiz, Esther, 2016. "The uncertainty of conditional returns, volatilities and correlations in DCC models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 170-185.
  8. Gensler, André & Sick, Bernhard & Vogt, Stephan, 2018. "A review of uncertainty representations and metaverification of uncertainty assessment techniques for renewable energies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 352-379.
  9. Ashish Arora & Michelle Gittelman & Sarah Kaplan & John Lynch & Will Mitchell & Nicolaj Siggelkow & Mei Li & Ying Lin & Shuai Huang & Craig Crossland, 2016. "The use of sparse inverse covariance estimation for relationship detection and hypothesis generation in strategic management," Strategic Management Journal, Wiley Blackwell, vol. 37(1), pages 86-97, January.
  10. Evangelos Spiliotis & Fotios Petropoulos & Konstantinos Nikolopoulos, 2020. "The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece," Energies, MDPI, vol. 13(8), pages 1-18, April.
  11. Taylor, James W. & Jeon, Jooyoung, 2015. "Forecasting wind power quantiles using conditional kernel estimation," Renewable Energy, Elsevier, vol. 80(C), pages 370-379.
  12. Giwhyun Lee & Yu Ding & Marc G. Genton & Le Xie, 2015. "Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 56-67, March.
  13. Nuño Martinez, Edgar & Cutululis, Nicolaos & Sørensen, Poul, 2018. "High dimensional dependence in power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 197-213.
  14. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
  15. Georgios Anastasiades & Patrick McSharry, 2013. "Quantile Forecasting of Wind Power Using Variability Indices," Energies, MDPI, vol. 6(2), pages 1-34, February.
  16. Aziz Ezzat, Ahmed, 2020. "Turbine-specific short-term wind speed forecasting considering within-farm wind field dependencies and fluctuations," Applied Energy, Elsevier, vol. 269(C).
  17. Xinxin Zhu & Marc Genton & Yingzhong Gu & Le Xie, 2014. "Space-time wind speed forecasting for improved power system dispatch," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 1-25, March.
  18. Haben, Stephen & Giasemidis, Georgios & Ziel, Florian & Arora, Siddharth, 2019. "Short term load forecasting and the effect of temperature at the low voltage level," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1469-1484.
  19. Yang Hu & Yilin Qiao & Jingchun Chu & Ling Yuan & Lei Pan, 2019. "Joint Point-Interval Prediction and Optimization of Wind Power Considering the Sequential Uncertainties of Stepwise Procedure," Energies, MDPI, vol. 12(11), pages 1-21, June.
  20. Ziel, Florian & Croonenbroeck, Carsten & Ambach, Daniel, 2016. "Forecasting wind power – Modeling periodic and non-linear effects under conditional heteroscedasticity," Applied Energy, Elsevier, vol. 177(C), pages 285-297.
  21. repec:hum:wpaper:sfb649dp2016-035 is not listed on IDEAS
  22. Haben, Stephen & Giasemidis, Georgios, 2016. "A hybrid model of kernel density estimation and quantile regression for GEFCom2014 probabilistic load forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1017-1022.
  23. Arora, Siddharth & Taylor, James W., 2016. "Forecasting electricity smart meter data using conditional kernel density estimation," Omega, Elsevier, vol. 59(PA), pages 47-59.
  24. Nasery, Praanjal & Aziz Ezzat, Ahmed, 2023. "Yaw-adjusted wind power curve modeling: A local regression approach," Renewable Energy, Elsevier, vol. 202(C), pages 1368-1376.
  25. Golparvar, Behzad & Papadopoulos, Petros & Ezzat, Ahmed Aziz & Wang, Ruo-Qian, 2021. "A surrogate-model-based approach for estimating the first and second-order moments of offshore wind power," Applied Energy, Elsevier, vol. 299(C).
  26. Ines Würth & Laura Valldecabres & Elliot Simon & Corinna Möhrlen & Bahri Uzunoğlu & Ciaran Gilbert & Gregor Giebel & David Schlipf & Anton Kaifel, 2019. "Minute-Scale Forecasting of Wind Power—Results from the Collaborative Workshop of IEA Wind Task 32 and 36," Energies, MDPI, vol. 12(4), pages 1-30, February.
  27. Xydas, Erotokritos & Qadrdan, Meysam & Marmaras, Charalampos & Cipcigan, Liana & Jenkins, Nick & Ameli, Hossein, 2017. "Probabilistic wind power forecasting and its application in the scheduling of gas-fired generators," Applied Energy, Elsevier, vol. 192(C), pages 382-394.
  28. Jeon, Jooyoung & Taylor, James W., 2016. "Short-term density forecasting of wave energy using ARMA-GARCH models and kernel density estimation," International Journal of Forecasting, Elsevier, vol. 32(3), pages 991-1004.
  29. Gallego-Castillo, Cristobal & Bessa, Ricardo & Cavalcante, Laura & Lopez-Garcia, Oscar, 2016. "On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power," Energy, Elsevier, vol. 113(C), pages 355-365.
  30. Amanda S. Hering & Karen Kazor & William Kleiber, 2015. "A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales," Resources, MDPI, vol. 4(1), pages 1-23, February.
  31. Long Cai & Jie Gu & Jinghuan Ma & Zhijian Jin, 2019. "Probabilistic Wind Power Forecasting Approach via Instance-Based Transfer Learning Embedded Gradient Boosting Decision Trees," Energies, MDPI, vol. 12(1), pages 1-19, January.
  32. Jeon, Jooyoung & Panagiotelis, Anastasios & Petropoulos, Fotios, 2019. "Probabilistic forecast reconciliation with applications to wind power and electric load," European Journal of Operational Research, Elsevier, vol. 279(2), pages 364-379.
  33. Lepore, Antonio & Palumbo, Biagio & Pievatolo, Antonio, 2020. "A Bayesian approach for site-specific wind rose prediction," Renewable Energy, Elsevier, vol. 150(C), pages 691-702.
  34. Li, Jianglong & Ho, Mun Sing, 2024. "End-year China wind power installation rush reduces electric system reliability," Energy Economics, Elsevier, vol. 133(C).
  35. Xu, Bing & Costa-Climent, Ricardo & Wang, Yanyan & Xiao, Yuan, 2020. "Financial support for micro and small enterprises: Economic benefit or social responsibility?," Journal of Business Research, Elsevier, vol. 115(C), pages 266-271.
  36. Taylor, James W., 2017. "Probabilistic forecasting of wind power ramp events using autoregressive logit models," European Journal of Operational Research, Elsevier, vol. 259(2), pages 703-712.
  37. Huber, Julian & Dann, David & Weinhardt, Christof, 2020. "Probabilistic forecasts of time and energy flexibility in battery electric vehicle charging," Applied Energy, Elsevier, vol. 262(C).
  38. Xinxin Zhu & Marc Genton & Yingzhong Gu & Le Xie, 2014. "Rejoinder on: Space-time wind speed forecasting for improved power system dispatch," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 45-50, March.
  39. Arora Siddharth & Little Max A. & McSharry Patrick E., 2013. "Nonlinear and nonparametric modeling approaches for probabilistic forecasting of the US gross national product," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 395-420, September.
  40. Xiaochun Meng & James W. Taylor & Souhaib Ben Taieb & Siran Li, 2020. "Scores for Multivariate Distributions and Level Sets," Papers 2002.09578, arXiv.org, revised Jun 2023.
  41. Antonio Bracale & Pasquale De Falco, 2015. "An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power," Energies, MDPI, vol. 8(9), pages 1-22, September.
  42. Hu, Yang & Xi, Yunhua & Pan, Chenyang & Li, Gengda & Chen, Baowei, 2020. "Daily condition monitoring of grid-connected wind turbine via high-fidelity power curve and its comprehensive rating," Renewable Energy, Elsevier, vol. 146(C), pages 2095-2111.
  43. Talari, Saber & Shafie-khah, Miadreza & Osório, Gerardo J. & Aghaei, Jamshid & Catalão, João P.S., 2018. "Stochastic modelling of renewable energy sources from operators' point-of-view: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1953-1965.
  44. Ambach, Daniel & Schmid, Wolfgang, 2017. "A new high-dimensional time series approach for wind speed, wind direction and air pressure forecasting," Energy, Elsevier, vol. 135(C), pages 833-850.
  45. Mohsen Vahidzadeh & Corey D. Markfort, 2019. "Modified Power Curves for Prediction of Power Output of Wind Farms," Energies, MDPI, vol. 12(9), pages 1-19, May.
  46. Mateusz Ciski & Krzysztof Rząsa & Marek Ogryzek, 2019. "Use of GIS Tools in Sustainable Heritage Management—The Importance of Data Generalization in Spatial Modeling," Sustainability, MDPI, vol. 11(20), pages 1-21, October.
  47. Leina Zhao & Yujia Bai & Sishi Zhang & Yanpeng Wang & Jie Kang & Wenxuan Zhang, 2022. "A Novel Hybrid Model for Short-Term Traffic Flow Prediction Based on Extreme Learning Machine and Improved Kernel Density Estimation," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
  48. Soukissian, Takvor H. & Karathanasi, Flora E., 2017. "On the selection of bivariate parametric models for wind data," Applied Energy, Elsevier, vol. 188(C), pages 280-304.
  49. López Cabrera, Brenda & Schulz, Franziska, 2016. "Time-adaptive probabilistic forecasts of electricity spot prices with application to risk management," SFB 649 Discussion Papers 2016-035, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  50. Park, Jungyeon & Alvarenga, Estêvão & Jeon, Jooyoung & Li, Ran & Petropoulos, Fotios & Kim, Hokyun & Ahn, Kwangwon, 2024. "Probabilistic forecast-based portfolio optimization of electricity demand at low aggregation levels," Applied Energy, Elsevier, vol. 353(PB).
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